World production of palm oil increased spectacularly in the last 20 years, especially in Indonesia and Malaysia. As the largest producer, good management in oil palm plantation is very important, the expansion of plantation also must be well planned, because its existence must not affect the surrounding environment. Therefore the information of oil palm age or condition of their growth is needed. Remote sensing has significant potential to aid oil palm monitoring and detection effort. It also provides a cost-effective method to these purpose and at same time provides side specific assessments of management areas, Synthetic Aperture Radar (SAR) is crucial for this task. The SAR is an active sensor that operates in all weather condition and daylight independent delivering information all year around at the time that is needed. SAR is sensitive to texture, size and orientation of structural objects, moisture content and ground conditions. This study has objectives to compare the methods that have been developed to monitor oil palm by using optic data and SAR data. The data that used are Landsat 8 and Sentinel-1.The study area is Asahan district North Sumatera. The regression analysis by using regression method indicates that oil palm age can be monitored by using NDVI or backscatter of SAR values with growth model. The R2 of model for Landsat 8 is 0.85 and 0.77 for Sentinel 1. Both models can be used for monitoring the condition and age of oil palm.

Information about oil palm phenology is required for oil palm plantation management, but using spaceborne polarimetric radar imagery remains challenging. However, spaceborne polarimetric radar on X-, C-, and L-band is promising on structure vegetation and cloud area. This study investigates the scattering model of oil palm phenology based on spaceborne X-, C-, and L-band polarimetric Synthetic Aperture Radar (SAR) imaging. The X-, C-, and L-band polarimetric SAR are derived from spaceborne of TerraSAR-X, Sentinel-1A, and ALOS PALSAR 2. Study area is located in oil palm plantations, Asahan District, North Sumatra, Indonesia. The methodology includes data collection, preprocessing, radiometric calibration, speckle filtering, terrain correction, extraction of scattering value, and development of scattering model of oil palm phenology. The results showed different scattering characteristics for the X-, C-, and L-band polarimetric SAR of oil palm for age and found the potential of the scattering model for oil palm phenology based on the X-band on HH polarization that showed a nonlinear model with R 2 = 0.65 . The C-band on VH and VV polarization showed a nonlinear model with R 2 = 0.56 and R 2 = 0.89 . The L-band on HV and HH polarization showed a logarithmic model with R 2 = 0.50 and R 2 = 0.51 . In this case, the most potential of the scattering model of oil palm phenology based on R 2 is using C-band on VV polarization. However, the scattering model based on X-, C-, and L-band is potentially to be used and applied to identify the phenology of oil palm in Indonesia, which is the main parameter in yield estimation. For the future phenology model needs to improve accuracy by integrating multisensors, including different wavelengths on optical and microwave sensors and more in situ data.

Abstract. Oil Palm (Elaeis guineensis Jack.) is one of the world's most important tropical tree crops.Its expansion has been reported to cause widespread environment impacts. SPOT 6 data is one of high resolution satellite data that can give information more detail about vegetation and the age of oil palm plantation. The objective of this study was to analyze the growth profile of oil palm and to estimate the productivity age of oil palm. The study area is PTP N 3 in Tebing Tinggi North Sumatera Indonesia.The method that used is NDVI analysis and regression analysis for getting the model of oil palm growth profile. Data from the field were collected as the secondary data to build that model. The data that collected were age of oil palm and diameters of canopy for every age. Results indicate that oil palm growth can be explained by variation of NDVI with formula y = -0.0004x 2 + 0.0107x + 0.3912, where x is oil palm age and Y is NDVI of SPOT, with R² = 0.657. This equation can be used to predict the age of oil palm for range 4 to 11 years with R 2 around 0.89.

Remote sensing application that used integrated with environmentally factors for oil palm yield estimating using Worldview-2 Imagery vegetation index (VI) was done. The aims of this study to get : 1) Red Edge Normalized Different Vegetation Index (RENDVI) and C h l o r o p h y l l I n d e x R e d E d g e ( C IRE ) ; 2) Correlation both of VI and oil palm yield; 3) oil palm yield estimation. The methods that used in this study were VI calculation by using RENDVI [(λNIR -λRED EDGE)/(λNIR +λRED EDGE )] and CIRE = [(λNIR /λRED EDGE )-1]. Oil EDGE NIR RED EDGE NIR RED EDGE palm yield estimation done by using linier regression and multiple linier regression. Linier regression used oil palm yield as dependent factor (Y) and VI as independent factor. Multiple linier regression used oil palm yield as dependent factor (Y), vegetative factors (oil palm yield, population per hectars, leaf area index) and environmentally factor (% clay, soil fertility index, altitude and water balance) as independent factors. The results of this study were: 1) the RENDVI value range -1 to 0.493 with average 0.30; while the CIRE value range -1 until 1.845 with average value 0.85. 2) The RENDVI dan CIRE have low positive linier correlation with oil pal yield rendah (rRENDVI = 0.355 dan rCIRE = 0.354); 3) Oil palm RENDVI CIRE yield estimation that using RENDVI and CIRE , vegetatation factors, environmentally factors data integration have similar correlation (r=0.763). Overall estimation model accuration get more than 90% estimation accuration on current month.

Ganoderma boninense is a major devastating disease for oil palm. The severity level identification of Ganoderma boninense on oil palm plantation is important to support the decision making on managerial activities. There have been researches conducted about the usage of unmanned aerial photograph (UAV) on oil palm plantation, nonetheless, the utilization of digital data on the visible aerial photograph has not optimally used. This study aims to obtain alternative methods to identify the severity level of Ganoderma boninense infection with visible spectral index from a visible aerial photograph (RGB). Visible aerial photograph (RGB-aerial photograph) is adopted on this research and carried out at Dusun Ulu plantation with various visible spectral-index methods. The visible spectral-index methods are the excess green index (ExG), the excess red index (ExR), the excess green minus excess red index, and the colour of vegetation extraction (CIVE). The results of four visible spectral-index methods are able to differentiate the severity level of Ganoderma boninense infection on each individual of oil palm.